AI-Powered Biomaterial Design for Tissue Engineering

This chapter explores the transformative convergence of artificial intelligence (AI) and tissue engineering, focusing on the accelerated design and optimization of biomaterials for regenerative medicine. Biomaterials serve as critical scaffolds that mimic native tissue structure and biochemical cues to support cell behavior and tissue regeneration. Traditional empirical approaches to biomaterial discovery are time-consuming, costly, and challenged by complex multi-parameter spaces. AI offers powerful data-driven solutions, including machine learning, deep learning, and generative models, that predict material properties, guide inverse design, and optimize fabrication parameters with unprecedented efficiency. The chapter reviews key AI methodologies applied to hydrogels, nanocomposites, and polymeric scaffolds, highlighting advances in predictive modeling, image analysis, and multi-objective optimization. It also addresses vital challenges such as data scarcity, quality, and integration of experimental and simulation workflows. Ethical, regulatory, and translational considerations for clinical implementation are discussed to ensure safety, reproducibility, and equity. By illustrating landmark case studies and emerging trends, this work provides a comprehensive roadmap for harnessing AI to drive innovation in intelligent biomaterial design, ultimately facilitating next-generation tissue repair and regeneration. The chapter is intended to equip researchers, graduate students, and professionals across biomedical engineering and computational sciences with foundational knowledge and future perspectives in AI-enabled tissue engineering.